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Stephenson JD, Totoo P, Burke D, Jänes J, Beltrao P, Martin M. ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Res 2024; 52:W140-W147. [PMID: 38769064 PMCID: PMC11223857 DOI: 10.1093/nar/gkae413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/26/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024] Open
Abstract
Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API.
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Affiliation(s)
| | - Prabhat Totoo
- EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, Cambridgeshire, UK
| | | | - Jürgen Jänes
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Pedro Beltrao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Maria J Martin
- EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, Cambridgeshire, UK
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2
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Livesey BJ, Badonyi M, Dias M, Frazer J, Kumar S, Lindorff-Larsen K, McCandlish DM, Orenbuch R, Shearer CA, Muffley L, Foreman J, Glazer AM, Lehner B, Marks DS, Roth FP, Rubin AF, Starita LM, Marsh JA. Guidelines for releasing a variant effect predictor. ARXIV 2024:arXiv:2404.10807v1. [PMID: 38699161 PMCID: PMC11065047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.
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Affiliation(s)
- Benjamin J. Livesey
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Mihaly Badonyi
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Mafalda Dias
- Centre for Genomic Regulation (CRG),The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jonathan Frazer
- Centre for Genomic Regulation (CRG),The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Sushant Kumar
- Department of Medical Biophysics, University of Toronto; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - David M. McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Rose Orenbuch
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | | | - Lara Muffley
- Department of Genome Sciences, University of Washington and the Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Julia Foreman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Ben Lehner
- Wellcome Sanger Institute, Cambridge, UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Debora S. Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Frederick P. Roth
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alan F. Rubin
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research; Department of Medical Biology, University of Melbourne, Parkville, Australia
| | - Lea M. Starita
- Department of Genome Sciences, University of Washington and the Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Joseph A. Marsh
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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3
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Richau CS, Scherer NDM, Matta BP, de Armas EM, de Barros Moreira FC, Bergmann A, Pereira Chaves CB, Boroni M, dos Santos ACE, Moreira MAM. BRCA1, BRCA2, and TP53 germline and somatic variants and clinicopathological characteristics of Brazilian patients with epithelial ovarian cancer. Cancer Med 2024; 13:e6729. [PMID: 38308422 PMCID: PMC10905552 DOI: 10.1002/cam4.6729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/20/2023] [Accepted: 11/07/2023] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Approximately 3/4 of ovarian cancers are diagnosed in advanced stages, with the high-grade epithelial ovarian carcinoma (EOC) accounting for 90% of the cases. EOC present high genomic instability and somatic loss-of-function variants in genes associated with homologous recombination mutational repair pathway (HR), such as BRCA1 and BRCA2, and in TP53. The identification of germline variants in HR genes in EOC is relevant for treatment of platinum resistant tumors and relapsed tumors with therapies based in synthetic lethality such as PARP inhibitors. Patients with somatic variants in HR genes may also benefit from these therapies. In this work was analyzed the frequency of somatic variants in BRCA1, BRCA2, and TP53 in an EOC cohort of Brazilian patients, estimating the proportion of variants in tumoral tissue and their association with progression-free survival and overall survival. METHODS The study was conducted with paired blood/tumor samples from 56 patients. Germline and tumoral sequences of BRCA1, BRCA2, and TP53 were obtained by massive parallel sequencing. The HaplotypeCaller method was used for calling germline variants, and somatic variants were called with Mutect2. RESULTS A total of 26 germline variants were found, and seven patients presented germline pathogenic or likely pathogenic variants in BRCA1 or BRCA2. The analysis of tumoral tissue identified 52 somatic variants in 41 patients, being 43 somatic variants affecting or likely affecting protein functionality. Survival analyses showed that tumor staging was associated with overall survival (OS), while the presence of somatic mutation in TP53 was not associated with OS or progression-free survival. CONCLUSION Frequency of pathogenic or likely pathogenic germline variants in BRCA1 and BRCA2 (12.5%) was lower in comparison with other studies. TP53 was the most altered gene in tumors, with 62.5% presenting likely non-functional or non-functional somatic variants, while eight 14.2% presented likely non-functional or non-functional somatic variants in BRCA1 or BRCA2.
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Affiliation(s)
| | | | - Bruna Palma Matta
- Tumoral Genetics and Virology ProgramInstituto Nacional de CâncerRio de JaneiroBrazil
- Present address:
Hospital BP ‐ A Beneficência Portuguesa de São PauloSão PauloBrazil
| | | | | | - Anke Bergmann
- Clinical EpidemiologyInstituto Nacional de CâncerRio de JaneiroBrazil
| | | | - Mariana Boroni
- Bioinformatics and Computational Biology LaboratoryInstituto Nacional de CâncerRio de JaneiroBrazil
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4
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Andorf CM, Haley OC, Hayford RK, Portwood JL, Harding S, Sen S, Cannon EK, Gardiner JM, Kim HS, Woodhouse MR. PanEffect: a pan-genome visualization tool for variant effects in maize. Bioinformatics 2024; 40:btae073. [PMID: 38337024 PMCID: PMC10881103 DOI: 10.1093/bioinformatics/btae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024] Open
Abstract
SUMMARY Understanding the effects of genetic variants is crucial for accurately predicting traits and functional outcomes. Recent approaches have utilized artificial intelligence and protein language models to score all possible missense variant effects at the proteome level for a single genome, but a reliable tool is needed to explore these effects at the pan-genome level. To address this gap, we introduce a new tool called PanEffect. We implemented PanEffect at MaizeGDB to enable a comprehensive examination of the potential effects of coding variants across 50 maize genomes. The tool allows users to visualize over 550 million possible amino acid substitutions in the B73 maize reference genome and to observe the effects of the 2.3 million natural variations in the maize pan-genome. Each variant effect score, calculated from the Evolutionary Scale Modeling (ESM) protein language model, shows the log-likelihood ratio difference between B73 and all variants in the pan-genome. These scores are shown using heatmaps spanning benign outcomes to potential functional consequences. In addition, PanEffect displays secondary structures and functional domains along with the variant effects, offering additional functional and structural context. Using PanEffect, researchers now have a platform to explore protein variants and identify genetic targets for crop enhancement. AVAILABILITY AND IMPLEMENTATION The PanEffect code is freely available on GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/PanEffect). A maize implementation of PanEffect and underlying datasets are available at MaizeGDB (https://www.maizegdb.org/effect/maize/).
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Affiliation(s)
- Carson M Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
- Department of Computer Science, Iowa State University, Ames, IA 50011, United States
| | - Olivia C Haley
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - Rita K Hayford
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - John L Portwood
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - Stephen Harding
- USDA-ARS, Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Peoria, IL 61604, United States
| | - Shatabdi Sen
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, United States
| | - Ethalinda K Cannon
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - Jack M Gardiner
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, United States
| | - Hye-Seon Kim
- USDA-ARS, Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Peoria, IL 61604, United States
| | - Margaret R Woodhouse
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
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5
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Radford EJ, Tan HK, Andersson MHL, Stephenson JD, Gardner EJ, Ironfield H, Waters AJ, Gitterman D, Lindsay S, Abascal F, Martincorena I, Kolesnik-Taylor A, Ng-Cordell E, Firth HV, Baker K, Perry JRB, Adams DJ, Gerety SS, Hurles ME. Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation. Nat Commun 2023; 14:7702. [PMID: 38057330 PMCID: PMC10700591 DOI: 10.1038/s41467-023-43041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/30/2023] [Indexed: 12/08/2023] Open
Abstract
Loss-of-function of DDX3X is a leading cause of neurodevelopmental disorders (NDD) in females. DDX3X is also a somatically mutated cancer driver gene proposed to have tumour promoting and suppressing effects. We perform saturation genome editing of DDX3X, testing in vitro the functional impact of 12,776 nucleotide variants. We identify 3432 functionally abnormal variants, in three distinct classes. We train a machine learning classifier to identify functionally abnormal variants of NDD-relevance. This classifier has at least 97% sensitivity and 99% specificity to detect variants pathogenic for NDD, substantially out-performing in silico predictors, and resolving up to 93% of variants of uncertain significance. Moreover, functionally-abnormal variants can account for almost all of the excess nonsynonymous DDX3X somatic mutations seen in DDX3X-driven cancers. Systematic maps of variant effects generated in experimentally tractable cell types have the potential to transform clinical interpretation of both germline and somatic disease-associated variation.
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Affiliation(s)
- Elizabeth J Radford
- Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
- Department of Paediatrics, University of Cambridge, Level 8, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Hong-Kee Tan
- Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
| | | | | | - Eugene J Gardner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | | | | | | | | | | | | | | | - Elise Ng-Cordell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Psychology, University of British Columbia, Vancouver, Canada
| | - Helen V Firth
- Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Kate Baker
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
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6
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Leutert M, Barente AS, Fukuda NK, Rodriguez-Mias RA, Villén J. The regulatory landscape of the yeast phosphoproteome. Nat Struct Mol Biol 2023; 30:1761-1773. [PMID: 37845410 PMCID: PMC10841839 DOI: 10.1038/s41594-023-01115-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 09/05/2023] [Indexed: 10/18/2023]
Abstract
The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. Here, to gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 min of stress exposure. We identify shared and perturbation-specific stress response programs, uncover loss of phosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase-substrate networks, as we exemplify with target of rapamycin signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks.
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Affiliation(s)
- Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Anthony S Barente
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Noelle K Fukuda
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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7
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Bradley D, Hogrebe A, Dandage R, Dubé AK, Leutert M, Dionne U, Chang A, Villén J, Landry CR. The fitness cost of spurious phosphorylation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.08.561337. [PMID: 37873463 PMCID: PMC10592693 DOI: 10.1101/2023.10.08.561337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The fidelity of signal transduction requires the binding of regulatory molecules to their cognate targets. However, the crowded cell interior risks off-target interactions between proteins that are functionally unrelated. How such off-target interactions impact fitness is not generally known, but quantifying this is required to understand the constraints faced by cell systems as they evolve. Here, we use the model organism S. cerevisiae to inducibly express tyrosine kinases. Because yeast lacks bona fide tyrosine kinases, most of the resulting tyrosine phosphorylation is spurious. This provides a suitable system to measure the impact of artificial protein interactions on fitness. We engineered 44 yeast strains each expressing a tyrosine kinase, and quantitatively analysed their phosphoproteomes. This analysis resulted in ~30,000 phosphosites mapping to ~3,500 proteins. Examination of the fitness costs in each strain revealed a strong correlation between the number of spurious pY sites and decreased growth. Moreover, the analysis of pY effects on protein structure and on protein function revealed over 1000 pY events that we predict to be deleterious. However, we also find that a large number of the spurious pY sites have a negligible effect on fitness, possibly because of their low stoichiometry. This result is consistent with our evolutionary analyses demonstrating a lack of phosphotyrosine counter-selection in species with bona fide tyrosine kinases. Taken together, our results suggest that, alongside the risk for toxicity, the cell can tolerate a large degree of non-functional crosstalk as interaction networks evolve.
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Affiliation(s)
- David Bradley
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexander Hogrebe
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Rohan Dandage
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Ugo Dionne
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexis Chang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Christian R Landry
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
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8
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Durand R, Jalbert-Ross J, Fijarczyk A, Dubé AK, Landry CR. Cross-feeding affects the target of resistance evolution to an antifungal drug. PLoS Genet 2023; 19:e1011002. [PMID: 37856537 PMCID: PMC10617708 DOI: 10.1371/journal.pgen.1011002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/31/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Pathogenic fungi are a cause of growing concern. Developing an efficient and safe antifungal is challenging because of the similar biological properties of fungal and host cells. Consequently, there is an urgent need to better understand the mechanisms underlying antifungal resistance to prolong the efficacy of current molecules. A major step in this direction would be to be able to predict or even prevent the acquisition of resistance. We leverage the power of experimental evolution to quantify the diversity of paths to resistance to the antifungal 5-fluorocytosine (5-FC), commercially known as flucytosine. We generated hundreds of independent 5-FC resistant mutants derived from two genetic backgrounds from wild isolates of Saccharomyces cerevisiae. Through automated pin-spotting, whole-genome and amplicon sequencing, we identified the most likely causes of resistance for most strains. Approximately a third of all resistant mutants evolved resistance through a pleiotropic drug response, a potentially novel mechanism in response to 5-FC, marked by cross-resistance to fluconazole. These cross-resistant mutants are characterized by a loss of respiration and a strong tradeoff in drug-free media. For the majority of the remaining two thirds, resistance was acquired through loss-of-function mutations in FUR1, which encodes an important enzyme in the metabolism of 5-FC. We describe conditions in which mutations affecting this particular step of the metabolic pathway are favored over known resistance mutations affecting a step upstream, such as the well-known target cytosine deaminase encoded by FCY1. This observation suggests that ecological interactions may dictate the identity of resistance hotspots.
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Affiliation(s)
- Romain Durand
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Jordan Jalbert-Ross
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
| | - Anna Fijarczyk
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Alexandre K. Dubé
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Christian R. Landry
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
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9
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Cagiada M, Bottaro S, Lindemose S, Schenstrøm SM, Stein A, Hartmann-Petersen R, Lindorff-Larsen K. Discovering functionally important sites in proteins. Nat Commun 2023; 14:4175. [PMID: 37443362 PMCID: PMC10345196 DOI: 10.1038/s41467-023-39909-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/02/2023] [Indexed: 07/15/2023] Open
Abstract
Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.
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Affiliation(s)
- Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sandro Bottaro
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Søren Lindemose
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Signe M Schenstrøm
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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10
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Dunham AS, Beltrao P, AlQuraishi M. High-throughput deep learning variant effect prediction with Sequence UNET. Genome Biol 2023; 24:110. [PMID: 37161576 PMCID: PMC10169183 DOI: 10.1186/s13059-023-02948-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/20/2023] [Indexed: 05/11/2023] Open
Abstract
Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult to scale, including recent deep learning models. We introduce Sequence UNET, a highly scalable deep learning architecture that classifies and predicts variant frequency from sequence alone using multi-scale representations from a fully convolutional compression/expansion architecture. It achieves comparable pathogenicity prediction to recent methods. We demonstrate scalability by analysing 8.3B variants in 904,134 proteins detected through large-scale proteomics. Sequence UNET runs on modest hardware with a simple Python package.
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Affiliation(s)
- Alistair S Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1RQ, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
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11
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Wightman DP, Savage JE, de Leeuw CA, Jansen IE, Posthuma D. Rare variant aggregation in 148,508 exomes identifies genes associated with proxy dementia. Sci Rep 2023; 13:2179. [PMID: 36750708 PMCID: PMC9905079 DOI: 10.1038/s41598-023-29108-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
Proxy phenotypes allow for the utilization of genetic data from large population cohorts to analyze late-onset diseases by using parental diagnoses as a proxy for genetic disease risk. Proxy phenotypes based on parental diagnosis status have been used in previous studies to identify common variants associated with Alzheimer's disease. As of yet, proxy phenotypes have not been used to identify genes associated with Alzheimer's disease through rare variants. Here we show that a proxy Alzheimer's disease/dementia phenotype can capture known Alzheimer's disease risk genes through rare variant aggregation. We generated a proxy Alzheimer's disease/dementia phenotype for 148,508 unrelated individuals of European ancestry in the UK biobank in order to perform exome-wide rare variant aggregation analyses to identify genes associated with proxy Alzheimer's disease/dementia. We identified four genes significantly associated with the proxy phenotype, three of which were significantly associated with proxy Alzheimer's disease/dementia in an independent replication cohort consisting of 197,506 unrelated individuals of European ancestry in the UK biobank. All three of the replicated genes have been previously associated with clinically diagnosed Alzheimer's disease (SORL1, TREM2, and TOMM40/APOE). We show that proxy Alzheimer's disease/dementia can be used to identify genes associated with Alzheimer's disease through rare variant aggregation.
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Affiliation(s)
- Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands.
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
| | - Iris E Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, The Netherlands
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12
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Radi MS, Munro LJ, Salcedo-Sora JE, Kim SH, Feist AM, Kell DB. Understanding Functional Redundancy and Promiscuity of Multidrug Transporters in E. coli under Lipophilic Cation Stress. MEMBRANES 2022; 12:1264. [PMID: 36557171 PMCID: PMC9783932 DOI: 10.3390/membranes12121264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/27/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Multidrug transporters (MDTs) are major contributors to microbial drug resistance and are further utilized for improving host phenotypes in biotechnological applications. Therefore, the identification of these MDTs and the understanding of their mechanisms of action in vivo are of great importance. However, their promiscuity and functional redundancy represent a major challenge towards their identification. Here, a multistep tolerance adaptive laboratory evolution (TALE) approach was leveraged to achieve this goal. Specifically, a wild-type E. coli K-12-MG1655 and its cognate knockout individual mutants ΔemrE, ΔtolC, and ΔacrB were evolved separately under increasing concentrations of two lipophilic cations, tetraphenylphosphonium (TPP+), and methyltriphenylphosphonium (MTPP+). The evolved strains showed a significant increase in MIC values of both cations and an apparent cross-cation resistance. Sequencing of all evolved mutants highlighted diverse mutational mechanisms that affect the activity of nine MDTs including acrB, mdtK, mdfA, acrE, emrD, tolC, acrA, mdtL, and mdtP. Besides regulatory mutations, several structural mutations were recognized in the proximal binding domain of acrB and the permeation pathways of both mdtK and mdfA. These details can aid in the rational design of MDT inhibitors to efficiently combat efflux-based drug resistance. Additionally, the TALE approach can be scaled to different microbes and molecules of medical and biotechnological relevance.
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Affiliation(s)
- Mohammad S. Radi
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
| | - Lachlan J. Munro
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
| | - Jesus E. Salcedo-Sora
- GeneMill, Shared Research Facilities, Faculty of Health and Life Sciences, University of Liverpool, Crown St., Liverpool L69 7ZB, UK
| | - Se Hyeuk Kim
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
| | - Adam M. Feist
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
- Department of Bioengineering, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA
| | - Douglas B. Kell
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St., Liverpool L69 7ZB, UK
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13
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Gress A, Srikakulam SK, Keller S, Ramensky V, Kalinina OV. d-StructMAn: Containerized structural annotation on the scale from genetic variants to whole proteomes. Gigascience 2022; 11:6706670. [PMID: 36130085 PMCID: PMC9487898 DOI: 10.1093/gigascience/giac086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/06/2022] [Accepted: 08/18/2022] [Indexed: 11/30/2022] Open
Abstract
Background Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution. Results We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins. Conclusions d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.
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Affiliation(s)
- Alexander Gress
- Correspondence address. Alexander Gress, Campus Saarland University 66123 Saarbrücken Building E2.1 Room 101; E-mail:
| | - Sanjay K Srikakulam
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany
- Interdisciplinary Graduate School of Natural Product Research, Saarland University, Saarbrücken 6: 119991, Germany
| | - Sebastian Keller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany
- Research Group Computational Biology, Max Planck Institute for Informatics, Saarbrücken 7: 66421, Germany
| | - Vasily Ramensky
- National Medical Research Center for Therapy and Preventive Medicine of the Ministry of Healthcare of Russian Federation, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany
- Medical Faculty, Saarland University, Homburg, Germany
- Center for Bioinformatics, Saarland Informatics Campus, Saarbrücken, Germany
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14
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Ma T, Li H, Zhang X. Discovering single-cell eQTLs from scRNA-seq data only. Gene 2022; 829:146520. [PMID: 35452708 DOI: 10.1016/j.gene.2022.146520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 01/12/2022] [Accepted: 04/15/2022] [Indexed: 12/14/2022]
Abstract
eQTL studies are essential for understanding genomic regulation. The effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, the current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single-cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at the single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a uniquely powerful tool for utilizing the vast scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.
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Affiliation(s)
- Tianxing Ma
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Haochen Li
- School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China; School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China.
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15
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Anand A, Patel A, Chen K, Olson CA, Phaneuf PV, Lamoureux C, Hefner Y, Szubin R, Feist AM, Palsson BO. Laboratory evolution of synthetic electron transport system variants reveals a larger metabolic respiratory system and its plasticity. Nat Commun 2022; 13:3682. [PMID: 35760776 PMCID: PMC9237125 DOI: 10.1038/s41467-022-30877-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
The bacterial respiratory electron transport system (ETS) is branched to allow condition-specific modulation of energy metabolism. There is a detailed understanding of the structural and biochemical features of respiratory enzymes; however, a holistic examination of the system and its plasticity is lacking. Here we generate four strains of Escherichia coli harboring unbranched ETS that pump 1, 2, 3, or 4 proton(s) per electron and characterized them using a combination of synergistic methods (adaptive laboratory evolution, multi-omic analyses, and computation of proteome allocation). We report that: (a) all four ETS variants evolve to a similar optimized growth rate, and (b) the laboratory evolutions generate specific rewiring of major energy-generating pathways, coupled to the ETS, to optimize ATP production capability. We thus define an Aero-Type System (ATS), which is a generalization of the aerobic bioenergetics and is a metabolic systems biology description of respiration and its inherent plasticity. The bacterial respiratory electron transport system (ETS) is branched to allow condition-specific modulation of energy metabolism. Here the authors examine the systems level properties of aerobic electron transport system using adaptive laboratory evolution and multi-omics analyses.
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Affiliation(s)
- Amitesh Anand
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. .,Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, Maharashtra, India.
| | - Arjun Patel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Ke Chen
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Connor A Olson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Patrick V Phaneuf
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Cameron Lamoureux
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Kongens, Lyngby, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. .,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Kongens, Lyngby, Denmark.
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16
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Marciano DC, Wang C, Hsu TK, Bourquard T, Atri B, Nehring RB, Abel NS, Bowling EA, Chen TJ, Lurie PD, Katsonis P, Rosenberg SM, Herman C, Lichtarge O. Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli. Nat Commun 2022; 13:3189. [PMID: 35680894 PMCID: PMC9184624 DOI: 10.1038/s41467-022-30889-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/24/2022] [Indexed: 11/08/2022] Open
Abstract
Since antibiotic development lags, we search for potential drug targets through directed evolution experiments. A challenge is that many resistance genes hide in a noisy mutational background as mutator clones emerge in the adaptive population. Here, to overcome this noise, we quantify the impact of mutations through evolutionary action (EA). After sequencing ciprofloxacin or colistin resistance strains grown under different mutational regimes, we find that an elevated sum of the evolutionary action of mutations in a gene identifies known resistance drivers. This EA integration approach also suggests new antibiotic resistance genes which are then shown to provide a fitness advantage in competition experiments. Moreover, EA integration analysis of clinical and environmental isolates of antibiotic resistant of E. coli identifies gene drivers of resistance where a standard approach fails. Together these results inform the genetic basis of de novo colistin resistance and support the robust discovery of phenotype-driving genes via the evolutionary action of genetic perturbations in fitness landscapes.
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Affiliation(s)
- David C Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Teng-Kuei Hsu
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Benu Atri
- Structural and Computational Biology & Molecular Biophysics Program, Baylor College of Medicine, Houston, TX, 77030, USA
- Clara Analytics Inc., 451 El Camino Real #201, Santa Clara, CA, 95050, USA
| | - Ralf B Nehring
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Nicholas S Abel
- Department of Pharmacology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Elizabeth A Bowling
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Taylor J Chen
- Integrative Molecular & Biomedical Biosciences Program, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Pamela D Lurie
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Susan M Rosenberg
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- The Verna and Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
- Integrative Molecular & Biomedical Biosciences Program, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christophe Herman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
- Structural and Computational Biology & Molecular Biophysics Program, Baylor College of Medicine, Houston, TX, 77030, USA.
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA.
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, 77030, USA.
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17
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Paiva VDA, Gomes IDS, Monteiro CR, Mendonça MV, Martins PM, Santana CA, Gonçalves-Almeida V, Izidoro SC, Melo-Minardi RCD, Silveira SDA. Protein structural bioinformatics: An overview. Comput Biol Med 2022; 147:105695. [DOI: 10.1016/j.compbiomed.2022.105695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 11/27/2022]
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18
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Martín M, Brunello FG, Modenutti CP, Nicola JP, Marti MA. MotSASi: Functional short linear motifs (SLiMs) prediction based on genomic single nucleotide variants and structural data. Biochimie 2022; 197:59-73. [DOI: 10.1016/j.biochi.2022.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/17/2022] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
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19
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Høie MH, Cagiada M, Beck Frederiksen AH, Stein A, Lindorff-Larsen K. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Rep 2022; 38:110207. [PMID: 35021073 DOI: 10.1016/j.celrep.2021.110207] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/01/2021] [Accepted: 12/13/2021] [Indexed: 01/23/2023] Open
Abstract
Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.
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Affiliation(s)
- Magnus Haraldson Høie
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Anders Haagen Beck Frederiksen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
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20
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Lutz S, Van Dyke K, Feraru MA, Albert FW. Multiple epistatic DNA variants in a single gene affect gene expression in trans. Genetics 2022; 220:iyab208. [PMID: 34791209 PMCID: PMC8733636 DOI: 10.1093/genetics/iyab208] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/09/2021] [Indexed: 01/08/2023] Open
Abstract
DNA variants that alter gene expression in trans are important sources of phenotypic variation. Nevertheless, the identity of trans-acting variants remains poorly understood. Single causal variants in several genes have been reported to affect the expression of numerous distant genes in trans. Whether these simple molecular architectures are representative of trans-acting variation is unknown. Here, we studied the large RAS signaling regulator gene IRA2, which contains variants with extensive trans-acting effects on gene expression in the yeast Saccharomyces cerevisiae. We used systematic CRISPR-based genome engineering and a sensitive phenotyping strategy to dissect causal variants to the nucleotide level. In contrast to the simple molecular architectures known so far, IRA2 contained at least seven causal nonsynonymous variants. The effects of these variants were modulated by nonadditive, epistatic interactions. Two variants at the 5'-end affected gene expression and growth only when combined with a third variant that also had no effect in isolation. Our findings indicate that the molecular basis of trans-acting genetic variation may be considerably more complex than previously appreciated.
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Affiliation(s)
- Sheila Lutz
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Krisna Van Dyke
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Matthew A Feraru
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
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21
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Walker ME, Watson TL, Large CRL, Berkovich Y, Lang TA, Dunham MJ, Formby S, Jiranek V. OUP accepted manuscript. FEMS Yeast Res 2022; 22:6574411. [PMID: 35472090 PMCID: PMC9329090 DOI: 10.1093/femsyr/foac022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 03/25/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
In winemaking, slow or stuck alcoholic fermentation can impact processing efficiency and wine quality. Residual fructose in the later stages of fermentation can leave the wine ‘out of specification’ unless removed, which requires reinoculation or use of a more fructophilic yeast. As such, robust, fermentation efficient strains are still highly desirable to reduce this risk. We report on a combined EMS mutagenesis and Directed Evolution (DE) approach as a ‘proof of concept’ to improve fructose utilization and decrease fermentation duration. One evolved isolate, Tee 9, was evaluated against the parent, AWRI 796 in defined medium (CDGJM) and Semillon juice. Interestingly, Tee 9 exhibited improved fermentation in CDGJM at several nitrogen contents, but not in juice. Genomic comparison between AWRI 796 and Tee 9 identified 371 mutations, but no chromosomal copy number variation. A total of 95 noncoding and 276 coding mutations were identified in 297 genes (180 of which encode proteins with one or more substitutions). Whilst introduction of two of these, Gid7 (E726K) or Fba1 (G135S), into AWRI 796 did not lead to the fermentation improvement seen in Tee 9, similar allelic swaps with the other mutations are needed to understand Tee 9’s adaption to CDGJM. Furthermore, the 378 isolates, potentially mutagenized but with the same genetic background, are likely a useful resource for future phenotyping and genome-wide association studies.
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Affiliation(s)
| | | | - Christopher R L Large
- Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, United States
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, United States
| | - Yan Berkovich
- Department of Wine Science, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
| | - Tom A Lang
- Department of Wine Science, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, United States
| | - Sean Formby
- Bioinformatics Graduate Program, University of British Columbia, Genome Sciences Centre, BCCA, 100-570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada
| | - Vladimir Jiranek
- Corresponding author: Department of Wine Science, The University of Adelaide, Urrbrae, SA 5064, Australia. Tel: +61 8 313 5561; E-mail:
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22
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Phaneuf PV, Zielinski DC, Yurkovich JT, Johnsen J, Szubin R, Yang L, Kim SH, Schulz S, Wu M, Dalldorf C, Ozdemir E, Lennen RM, Palsson BO, Feist AM. Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data. ACS Synth Biol 2021; 10:3379-3395. [PMID: 34762392 PMCID: PMC8870144 DOI: 10.1021/acssynbio.1c00337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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Microbes are being
engineered for an increasingly large and diverse
set of applications. However, the designing of microbial genomes remains
challenging due to the general complexity of biological systems. Adaptive
Laboratory Evolution (ALE) leverages nature’s problem-solving
processes to generate optimized genotypes currently inaccessible to
rational methods. The large amount of public ALE data now represents
a new opportunity for data-driven strain design. This study describes
how novel strain designs, or genome sequences not yet observed in
ALE experiments or published designs, can be extracted from aggregated
ALE data and demonstrates this by designing, building, and testing
three novel Escherichia coli strains with fitnesses
comparable to ALE mutants. These designs were achieved through a meta-analysis
of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental
conditions, 357 independent evolutions, and 13 957 observed
mutations), which additionally revealed global ALE mutation trends
that inform on ALE-derived strain design principles. Such informative
trends anticipate ALE-derived strain designs as largely gene-centric,
as opposed to noncoding, and composed of a relatively small number
of beneficial variants (approximately 6). These results demonstrate
how strain design efforts can be enhanced by the meta-analysis of
aggregated ALE data.
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Affiliation(s)
- Patrick V. Phaneuf
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, California 92093, United States
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, United States
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - James T. Yurkovich
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Josefin Johnsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Lei Yang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Se Hyeuk Kim
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Sebastian Schulz
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Muyao Wu
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Christopher Dalldorf
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Emre Ozdemir
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Rebecca M. Lennen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Bernhard O. Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, California 92093, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, United States
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Adam M. Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, United States
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
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23
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Mozzachiodi S, Tattini L, Llored A, Irizar A, Škofljanc N, D'Angiolo M, De Chiara M, Barré BP, Yue JX, Lutazi A, Loeillet S, Laureau R, Marsit S, Stenberg S, Albaud B, Persson K, Legras JL, Dequin S, Warringer J, Nicolas A, Liti G. Aborting meiosis allows recombination in sterile diploid yeast hybrids. Nat Commun 2021; 12:6564. [PMID: 34772931 PMCID: PMC8589840 DOI: 10.1038/s41467-021-26883-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 10/20/2021] [Indexed: 12/03/2022] Open
Abstract
Hybrids between diverged lineages contain novel genetic combinations but an impaired meiosis often makes them evolutionary dead ends. Here, we explore to what extent an aborted meiosis followed by a return-to-growth (RTG) promotes recombination across a panel of 20 Saccharomyces cerevisiae and S. paradoxus diploid hybrids with different genomic structures and levels of sterility. Genome analyses of 275 clones reveal that RTG promotes recombination and generates extensive regions of loss-of-heterozygosity in sterile hybrids with either a defective meiosis or a heavily rearranged karyotype, whereas RTG recombination is reduced by high sequence divergence between parental subgenomes. The RTG recombination preferentially arises in regions with low local heterozygosity and near meiotic recombination hotspots. The loss-of-heterozygosity has a profound impact on sexual and asexual fitness, and enables genetic mapping of phenotypic differences in sterile lineages where linkage analysis would fail. We propose that RTG gives sterile yeast hybrids access to a natural route for genome recombination and adaptation.
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Grants
- This work was supported by Agence Nationale de la Recherche (ANR-11-LABX-0028-01, ANR-13-BSV6-0006-01, ANR-15-IDEX-01, ANR-16-CE12-0019 and ANR-18-CE12-0004), Fondation pour la Recherche Médicale (FRM EQU202003010413), CEFIPRA, Cancéropôle PACA (AAP Equipment 2018), Meiogenix and the Swedish Research Council (2014-6547, 2014-4605 and 2018-03638). S.Mo. is funded by the convention CIFRE 2016/0582 between Meiogenix and ANRT. The Institut Curie NGS platform is supported by ANR-10-EQPX-03 (Equipex), ANR-10-INBS-09-08 (France Génomique Consortium), ITMO-CANCER and SiRIC INCA-DGOS (4654 program).
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Affiliation(s)
- Simone Mozzachiodi
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
- Meiogenix, 38, rue Servan, Paris, 75011, France
| | | | - Agnes Llored
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
| | | | - Neža Škofljanc
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
| | | | | | | | - Jia-Xing Yue
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Angela Lutazi
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
| | - Sophie Loeillet
- Institut Curie, Centre de Recherche, CNRS-UMR3244, PSL Research University, Paris, 75005, France
| | - Raphaelle Laureau
- Institut Curie, Centre de Recherche, CNRS-UMR3244, PSL Research University, Paris, 75005, France
- Department of Genetics and Development, Hammer Health Sciences Center, Columbia University Medical Center, New York, NY, USA
| | - Souhir Marsit
- Institut Curie, Centre de Recherche, CNRS-UMR3244, PSL Research University, Paris, 75005, France
- SPO, Université Montpellier, INRAE, Montpellier SupAgro, Montpellier, France
- Département de Biologie Chimie et Géographie, Université du Québec à Rimouski, Rimouski, Québec, Canada
| | - Simon Stenberg
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Benoit Albaud
- Institut Curie, ICGEX NGS Platform, Paris, 75005, France
| | - Karl Persson
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Jean-Luc Legras
- SPO, Université Montpellier, INRAE, Montpellier SupAgro, Montpellier, France
| | - Sylvie Dequin
- SPO, Université Montpellier, INRAE, Montpellier SupAgro, Montpellier, France
| | - Jonas Warringer
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Alain Nicolas
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
- Meiogenix, 38, rue Servan, Paris, 75011, France
- Institut Curie, Centre de Recherche, CNRS-UMR3244, PSL Research University, Paris, 75005, France
| | - Gianni Liti
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France.
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24
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Restoring fertility in yeast hybrids: Breeding and quantitative genetics of beneficial traits. Proc Natl Acad Sci U S A 2021; 118:2101242118. [PMID: 34518218 PMCID: PMC8463882 DOI: 10.1073/pnas.2101242118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2021] [Indexed: 11/18/2022] Open
Abstract
Hybrids between species can harbor a combination of beneficial traits from each parent and may exhibit hybrid vigor, more readily adapting to new harsher environments. Interspecies hybrids are also sterile and therefore an evolutionary dead end unless fertility is restored, usually via auto-polyploidisation events. In the Saccharomyces genus, hybrids are readily found in nature and in industrial settings, where they have adapted to severe fermentative conditions. Due to their hybrid sterility, the development of new commercial yeast strains has so far been primarily conducted via selection methods rather than via further breeding. In this study, we overcame infertility by creating tetraploid intermediates of Saccharomyces interspecies hybrids to allow continuous multigenerational breeding. We incorporated nuclear and mitochondrial genetic diversity within each parental species, allowing for quantitative genetic analysis of traits exhibited by the hybrids and for nuclear-mitochondrial interactions to be assessed. Using pooled F12 generation segregants of different hybrids with extreme phenotype distributions, we identified quantitative trait loci (QTLs) for tolerance to high and low temperatures, high sugar concentration, high ethanol concentration, and acetic acid levels. We identified QTLs that are species specific, that are shared between species, as well as hybrid specific, in which the variants do not exhibit phenotypic differences in the original parental species. Moreover, we could distinguish between mitochondria-type-dependent and -independent traits. This study tackles the complexity of the genetic interactions and traits in hybrid species, bringing hybrids into the realm of full genetic analysis of diploid species, and paves the road for the biotechnological exploitation of yeast biodiversity.
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25
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A global map of associations between types of protein posttranslational modifications and human genetic diseases. iScience 2021; 24:102917. [PMID: 34430807 PMCID: PMC8365368 DOI: 10.1016/j.isci.2021.102917] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/27/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022] Open
Abstract
There are >200 types of protein posttranslational modifications (PTMs) described in eukaryotes, each with unique proteome coverage and functions. We hypothesized that some genetic diseases may be caused by the removal of a specific type of PTMs by genomic variants and the consequent deregulation of particular functions. We collected >320,000 human PTMs representing 59 types and crossed them with >4M nonsynonymous DNA variants annotated with predicted pathogenicity and disease associations. We report >1.74M PTM-variant co-occurrences that an enrichment analysis distributed into 215 pairwise associations between 18 PTM types and 148 genetic diseases. Of them, 42% were not previously described. Removal of lysine acetylation exerts the most pronounced effect, and less studied PTM types such as S-glutathionylation or S-nitrosylation show relevance. Using pathogenicity predictions, we identified PTM sites that may produce particular diseases if prevented. Our results provide evidence of a substantial impact of PTM-specific removal on the pathogenesis of genetic diseases and phenotypes. There is an enrichment of disease-associated nsSNVs preventing certain types of PTMs We report 215 pairwise associations between 18 PTM types and 148 genetic diseases The removal of lysine acetylation exerts the most pronounced effect We report a set of PTM sites that may produce particular diseases if prevented
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26
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Moesslacher CS, Kohlmayr JM, Stelzl U. Exploring absent protein function in yeast: assaying post translational modification and human genetic variation. MICROBIAL CELL (GRAZ, AUSTRIA) 2021; 8:164-183. [PMID: 34395585 PMCID: PMC8329848 DOI: 10.15698/mic2021.08.756] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/13/2021] [Accepted: 06/18/2021] [Indexed: 01/08/2023]
Abstract
Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are absent. It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.
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Affiliation(s)
- Christina S Moesslacher
- Institute of Pharmaceutical Sciences and BioTechMed-Graz, University of Graz, Graz, Austria
- Contributed equally to the writing of this review
| | - Johanna M Kohlmayr
- Institute of Pharmaceutical Sciences and BioTechMed-Graz, University of Graz, Graz, Austria
- Contributed equally to the writing of this review
| | - Ulrich Stelzl
- Institute of Pharmaceutical Sciences and BioTechMed-Graz, University of Graz, Graz, Austria
- Contributed equally to the writing of this review
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27
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Silk M, Pires DEV, Rodrigues CHM, D'Souza EN, Olshansky M, Thorne N, Ascher DB. MTR3D: identifying regions within protein tertiary structures under purifying selection. Nucleic Acids Res 2021; 49:W438-W445. [PMID: 34050760 PMCID: PMC8265191 DOI: 10.1093/nar/gkab428] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 04/23/2021] [Accepted: 05/19/2021] [Indexed: 01/08/2023] Open
Abstract
The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21–41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5–8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann–Whitney U test P < 2.2 × 10−16). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/.
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Affiliation(s)
- Michael Silk
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia
| | - Elston N D'Souza
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia
| | - Moshe Olshansky
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Natalie Thorne
- Melbourne Genomics Health Alliance, Melbourne, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Melbourne, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Australia.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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28
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Šoštarić N, Arslan A, Carvalho B, Plech M, Voordeckers K, Verstrepen KJ, van Noort V. Integrated Multi-Omics Analysis of Mechanisms Underlying Yeast Ethanol Tolerance. J Proteome Res 2021; 20:3840-3852. [PMID: 34236875 PMCID: PMC8353626 DOI: 10.1021/acs.jproteome.1c00139] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
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For yeast cells,
tolerance to high levels of ethanol is vital both
in their natural environment and in industrially relevant conditions.
We recently genotyped experimentally evolved yeast strains adapted
to high levels of ethanol and identified mutations linked to ethanol
tolerance. In this study, by integrating genomic sequencing data with
quantitative proteomics profiles from six evolved strains (data set
identifier PXD006631) and construction of protein interaction networks,
we elucidate exactly how the genotype and phenotype are related at
the molecular level. Our multi-omics approach points to the rewiring
of numerous metabolic pathways affected by genomic and proteomic level
changes, from energy-producing and lipid pathways to differential
regulation of transposons and proteins involved in cell cycle progression.
One of the key differences is found in the energy-producing metabolism,
where the ancestral yeast strain responds to ethanol by switching
to respiration and employing the mitochondrial electron transport
chain. In contrast, the ethanol-adapted strains appear to have returned
back to energy production mainly via glycolysis and ethanol fermentation,
as supported by genomic and proteomic level changes. This work is
relevant for synthetic biology where systems need to function under
stressful conditions, as well as for industry and in cancer biology,
where it is important to understand how the genotype relates to the
phenotype.
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Affiliation(s)
- Nikolina Šoštarić
- Centre of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 22, B-3001 Leuven, Belgium
| | - Ahmed Arslan
- Centre of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 22, B-3001 Leuven, Belgium
| | - Bernardo Carvalho
- Centre of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 22, B-3001 Leuven, Belgium
| | - Marcin Plech
- Centre of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 22, B-3001 Leuven, Belgium.,VIB-KU Leuven Center for Microbiology, Bioincubator, Gaston Geenslaan 1, B-3001 Leuven, Belgium
| | - Karin Voordeckers
- Centre of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 22, B-3001 Leuven, Belgium.,VIB-KU Leuven Center for Microbiology, Bioincubator, Gaston Geenslaan 1, B-3001 Leuven, Belgium
| | - Kevin J Verstrepen
- Centre of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 22, B-3001 Leuven, Belgium.,VIB-KU Leuven Center for Microbiology, Bioincubator, Gaston Geenslaan 1, B-3001 Leuven, Belgium
| | - Vera van Noort
- Centre of Microbial and Plant Genetics, KU Leuven, Kasteelpark Arenberg 22, B-3001 Leuven, Belgium.,Institute of Biology Leiden, Faculty of Science, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
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29
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Liu Y, Lin Y, Guo Y, Wu F, Zhang Y, Qi X, Wang Z, Wang Q. Stress tolerance enhancement via SPT15 base editing in Saccharomyces cerevisiae. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:155. [PMID: 34229745 PMCID: PMC8259078 DOI: 10.1186/s13068-021-02005-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/26/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND Saccharomyces cerevisiae is widely used in traditional brewing and modern fermentation industries to produce biofuels, chemicals and other bioproducts, but challenged by various harsh industrial conditions, such as hyperosmotic, thermal and ethanol stresses. Thus, its stress tolerance enhancement has been attracting broad interests. Recently, CRISPR/Cas-based genome editing technology offers unprecedented tools to explore genetic modifications and performance improvement of S. cerevisiae. RESULTS Here, we presented that the Target-AID (activation-induced cytidine deaminase) base editor of enabling C-to-T substitutions could be harnessed to generate in situ nucleotide changes on the S. cerevisiae genome, thereby introducing protein point mutations in cells. The general transcription factor gene SPT15 was targeted, and total 36 mutants with diversified stress tolerances were obtained. Among them, the 18 tolerant mutants against hyperosmotic, thermal and ethanol stresses showed more than 1.5-fold increases of fermentation capacities. These mutations were mainly enriched at the N-terminal region and the convex surface of the saddle-shaped structure of Spt15. Comparative transcriptome analysis of three most stress-tolerant (A140G, P169A and R238K) and two most stress-sensitive (S118L and L214V) mutants revealed common and distinctive impacted global transcription reprogramming and transcriptional regulatory hubs in response to stresses, and these five amino acid changes had different effects on the interactions of Spt15 with DNA and other proteins in the RNA Polymerase II transcription machinery according to protein structure alignment analysis. CONCLUSIONS Taken together, our results demonstrated that the Target-AID base editor provided a powerful tool for targeted in situ mutagenesis in S. cerevisiae and more potential targets of Spt15 residues for enhancing yeast stress tolerance.
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Affiliation(s)
- Yanfang Liu
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuping Lin
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yufeng Guo
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Fengli Wu
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Yuanyuan Zhang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Xianni Qi
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Zhen Wang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinhong Wang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
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30
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Identification of phosphosites that alter protein thermal stability. Nat Methods 2021; 18:760-762. [PMID: 34140699 PMCID: PMC8783534 DOI: 10.1038/s41592-021-01178-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 05/05/2021] [Indexed: 02/03/2023]
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31
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Kędziora A, Speruda M, Wernecki M, Dudek B, Kapczynska K, Krzyżewska E, Rybka J, Bugla-Płoskońska G. How Bacteria Change after Exposure to Silver Nanoformulations: Analysis of the Genome and Outer Membrane Proteome. Pathogens 2021; 10:pathogens10070817. [PMID: 34209937 PMCID: PMC8308822 DOI: 10.3390/pathogens10070817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/21/2021] [Accepted: 06/24/2021] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE the main purpose of this work was to compare the genetic and phenotypic changes of E. coli treated with silver nanoformulations (E. coli BW25113 wt, E. coli BW25113 AgR, E. coli J53, E. coli ATCC 11229 wt, E. coli ATCC 11229 var. S2 and E. coli ATCC 11229 var. S7). Silver, as the metal with promising antibacterial properties, is currently widely used in medicine and the biomedical industry, in both ionic and nanoparticles forms. Silver nanoformulations are usually considered as one type of antibacterial agent, but their physical and chemical properties determine the way of interactions with the bacterial cell, the mode of action, and the bacterial cell response to silver. METHODS the changes in the bacterial genome, resulting from the treatment of bacteria with various silver nanoformulations, were verified by analyzing of genes (selected with mutfunc) and their conservative and non-conservative mutations selected with BLOSUM62. The phenotype was verified using an outer membrane proteome analysis (OMP isolation, 2-DE electrophoresis, and MS protein identification). RESULTS the variety of genetic and phenotypic changes in E. coli strains depends on the type of silver used for bacteria treatment. The most changes were identified in E. coli ATCC 11229 treated with silver nanoformulation signed as S2 (E. coli ATCC 11229 var. S2). We pinpointed 39 genes encoding proteins located in the outer membrane, 40 genes of their regulators, and 22 genes related to other outer membrane structures, such as flagellum, fimbria, lipopolysaccharide (LPS), or exopolysaccharide in this strain. Optical density of OmpC protein in E. coli electropherograms decreased after exposure to silver nanoformulation S7 (noticed in E. coli ATCC 11229 var. S7), and increased after treatment with the other silver nanoformulations (SNF) marked as S2 (noticed in E. coli ATCC 11229 var. S2). Increase of FliC protein optical density was identified in turn after Ag+ treatment (noticed in E.coli AgR). CONCLUSION the results show that silver nanoformulations (SNF) exerts a selective pressure on bacteria causing both conservative and non-conservative mutations. The proteomic approach revealed that the levels of some proteins have changed after treatment with appropriate SNF.
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Affiliation(s)
- Anna Kędziora
- Department of Microbiology, Faculty of Biological Sciences, University of Wroclaw, 51-148 Wroclaw, Poland; (M.S.); (M.W.); (B.D.)
- Correspondence: (A.K.); (G.B.-P.); Tel.: +487-1375-6323 (A.K.)
| | - Mateusz Speruda
- Department of Microbiology, Faculty of Biological Sciences, University of Wroclaw, 51-148 Wroclaw, Poland; (M.S.); (M.W.); (B.D.)
| | - Maciej Wernecki
- Department of Microbiology, Faculty of Biological Sciences, University of Wroclaw, 51-148 Wroclaw, Poland; (M.S.); (M.W.); (B.D.)
| | - Bartłomiej Dudek
- Department of Microbiology, Faculty of Biological Sciences, University of Wroclaw, 51-148 Wroclaw, Poland; (M.S.); (M.W.); (B.D.)
| | - Katarzyna Kapczynska
- Department of Immunology of Infectious Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland; (K.K.); (E.K.); (J.R.)
| | - Eva Krzyżewska
- Department of Immunology of Infectious Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland; (K.K.); (E.K.); (J.R.)
| | - Jacek Rybka
- Department of Immunology of Infectious Diseases, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland; (K.K.); (E.K.); (J.R.)
| | - Gabriela Bugla-Płoskońska
- Department of Microbiology, Faculty of Biological Sciences, University of Wroclaw, 51-148 Wroclaw, Poland; (M.S.); (M.W.); (B.D.)
- Correspondence: (A.K.); (G.B.-P.); Tel.: +487-1375-6323 (A.K.)
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Gersing SK, Wang Y, Grønbæk-Thygesen M, Kampmeyer C, Clausen L, Willemoës M, Andréasson C, Stein A, Lindorff-Larsen K, Hartmann-Petersen R. Mapping the degradation pathway of a disease-linked aspartoacylase variant. PLoS Genet 2021; 17:e1009539. [PMID: 33914734 PMCID: PMC8084241 DOI: 10.1371/journal.pgen.1009539] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 04/06/2021] [Indexed: 11/19/2022] Open
Abstract
Canavan disease is a severe progressive neurodegenerative disorder that is characterized by swelling and spongy degeneration of brain white matter. The disease is genetically linked to polymorphisms in the aspartoacylase (ASPA) gene, including the substitution C152W. ASPA C152W is associated with greatly reduced protein levels in cells, yet biophysical experiments suggest a wild-type like thermal stability. Here, we use ASPA C152W as a model to investigate the degradation pathway of a disease-causing protein variant. When we expressed ASPA C152W in Saccharomyces cerevisiae, we found a decreased steady state compared to wild-type ASPA as a result of increased proteasomal degradation. However, molecular dynamics simulations of ASPA C152W did not substantially deviate from wild-type ASPA, indicating that the native state is structurally preserved. Instead, we suggest that the C152W substitution interferes with the de novo folding pathway resulting in increased proteasomal degradation before reaching its stable conformation. Systematic mapping of the protein quality control components acting on misfolded and aggregation-prone species of C152W, revealed that the degradation is highly dependent on the molecular chaperone Hsp70, its co-chaperone Hsp110 as well as several quality control E3 ubiquitin-protein ligases, including Ubr1. In addition, the disaggregase Hsp104 facilitated refolding of aggregated ASPA C152W, while Cdc48 mediated degradation of insoluble ASPA protein. In human cells, ASPA C152W displayed increased proteasomal turnover that was similarly dependent on Hsp70 and Hsp110. Our findings underscore the use of yeast to determine the protein quality control components involved in the degradation of human pathogenic variants in order to identify potential therapeutic targets.
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Affiliation(s)
- Sarah K. Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yong Wang
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Grønbæk-Thygesen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Kampmeyer
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Lene Clausen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Willemoës
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Claes Andréasson
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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33
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Cagiada M, Johansson KE, Valanciute A, Nielsen SV, Hartmann-Petersen R, Yang JJ, Fowler DM, Stein A, Lindorff-Larsen K. Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance. Mol Biol Evol 2021; 38:3235-3246. [PMID: 33779753 PMCID: PMC8321532 DOI: 10.1093/molbev/msab095] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Understanding and predicting how amino acid substitutions affect proteins are keys to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analyzed 6,749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyze the structural and mechanistic origins of loss of function and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.
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Affiliation(s)
- Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kristoffer E Johansson
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Audrone Valanciute
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sofie V Nielsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jun J Yang
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, USA.,Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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Zhou Y, Zhao J, Fang J, Martin W, Li L, Nussinov R, Chan TA, Eng C, Cheng F. My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype. Genome Biol 2021; 22:53. [PMID: 33514395 PMCID: PMC7845113 DOI: 10.1186/s13059-021-02269-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 01/13/2021] [Indexed: 12/12/2022] Open
Abstract
Massive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces ("edgetic") and 311,022 functional sites ("nodetic"), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org .
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Junfei Zhao
- Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY, 10032, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, 10032, USA
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - William Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Timothy A Chan
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Center for Immunotherapy and Precision Immuno-Oncology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA.
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
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35
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Clausen L, Stein A, Grønbæk-Thygesen M, Nygaard L, Søltoft CL, Nielsen SV, Lisby M, Ravid T, Lindorff-Larsen K, Hartmann-Petersen R. Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation. PLoS Genet 2020; 16:e1009187. [PMID: 33137092 PMCID: PMC7660926 DOI: 10.1371/journal.pgen.1009187] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/12/2020] [Accepted: 10/10/2020] [Indexed: 01/24/2023] Open
Abstract
Germline mutations in the folliculin (FLCN) tumor suppressor gene are linked to Birt-Hogg-Dubé (BHD) syndrome, a dominantly inherited genetic disease characterized by predisposition to fibrofolliculomas, lung cysts, and renal cancer. Most BHD-linked FLCN variants include large deletions and splice site aberrations predicted to cause loss of function. The mechanisms by which missense variants and short in-frame deletions in FLCN trigger disease are unknown. Here, we present an integrated computational and experimental study that reveals that the majority of such disease-causing FLCN variants cause loss of function due to proteasomal degradation of the encoded FLCN protein, rather than directly ablating FLCN function. Accordingly, several different single-site FLCN variants are present at strongly reduced levels in cells. In line with our finding that FLCN variants are protein quality control targets, several are also highly insoluble and fail to associate with the FLCN-binding partners FNIP1 and FNIP2. The lack of FLCN binding leads to rapid proteasomal degradation of FNIP1 and FNIP2. Half of the tested FLCN variants are mislocalized in cells, and one variant (ΔE510) forms perinuclear protein aggregates. A yeast-based stability screen revealed that the deubiquitylating enzyme Ubp15/USP7 and molecular chaperones regulate the turnover of the FLCN variants. Lowering the temperature led to a stabilization of two FLCN missense proteins, and for one (R362C), function was re-established at low temperature. In conclusion, we propose that most BHD-linked FLCN missense variants and small in-frame deletions operate by causing misfolding and degradation of the FLCN protein, and that stabilization and resulting restoration of function may hold therapeutic potential of certain disease-linked variants. Our computational saturation scan encompassing both missense variants and single site deletions in FLCN may allow classification of rare FLCN variants of uncertain clinical significance.
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Affiliation(s)
- Lene Clausen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Grønbæk-Thygesen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Lasse Nygaard
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Cecilie L. Søltoft
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sofie V. Nielsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Michael Lisby
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Tommer Ravid
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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36
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Iqbal S, Hoksza D, Pérez-Palma E, May P, Jespersen JB, Ahmed SS, Rifat ZT, Heyne HO, Rahman MS, Cottrell JR, Wagner FF, Daly MJ, Campbell AJ, Lal D. MISCAST: MIssense variant to protein StruCture Analysis web SuiTe. Nucleic Acids Res 2020; 48:W132-W139. [PMID: 32402084 PMCID: PMC7319582 DOI: 10.1093/nar/gkaa361] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/17/2020] [Accepted: 05/11/2020] [Indexed: 12/19/2022] Open
Abstract
Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like 'Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?', or 'Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?' are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.
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Affiliation(s)
- Sumaiya Iqbal
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - David Hoksza
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.,Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
| | - Eduardo Pérez-Palma
- Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jakob B Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Shehab S Ahmed
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Zaara T Rifat
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Henrike O Heyne
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00100 Helsinki, Finland
| | - M Sohel Rahman
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Jeffrey R Cottrell
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Florence F Wagner
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark J Daly
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00100 Helsinki, Finland
| | - Arthur J Campbell
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dennis Lal
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA.,Cologne Center for Genomics, University of Cologne, Cologne, Germany.,Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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37
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Zhou Y, Dagli Hernandez C, Lauschke VM. Population-scale predictions of DPD and TPMT phenotypes using a quantitative pharmacogene-specific ensemble classifier. Br J Cancer 2020; 123:1782-1789. [PMID: 32973300 PMCID: PMC7722893 DOI: 10.1038/s41416-020-01084-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/26/2020] [Accepted: 09/02/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Inter-individual differences in dihydropyrimidine dehydrogenase (DPYD encoding DPD) and thiopurine S-methyltransferase (TPMT) activity are important predictors for fluoropyrimidine and thiopurine toxicity. While several variants in these genes are known to decrease enzyme activities, many additional genetic variations with unclear functional consequences have been identified, complicating informed clinical decision-making in the respective carriers. METHODS We used a novel pharmacogenetically trained ensemble classifier to analyse DPYD and TPMT genetic variability based on sequencing data from 138,842 individuals across eight populations. RESULTS The algorithm accurately predicted in vivo consequences of DPYD and TPMT variants (accuracy 91.4% compared to 95.3% in vitro). Further analysis showed high genetic complexity of DPD deficiency, advocating for sequencing-based DPYD profiling, whereas genotyping of four variants in TPMT was sufficient to explain >95% of phenotypic TPMT variability. Lastly, we provided population-scale profiles of ethnogeographic variability in DPD and TPMT phenotypes, and revealed striking interethnic differences in frequency and genetic constitution of DPD and TPMT deficiency. CONCLUSION These results provide the most comprehensive data set of DPYD and TPMT variability published to date with important implications for population-adjusted genetic profiling strategies of fluoropyrimidine and thiopurine risk factors and precision public health.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Carolina Dagli Hernandez
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden.,Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, 05508-000, Sao Paulo, Brazil
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden.
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38
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Mirauta BA, Seaton DD, Bensaddek D, Brenes A, Bonder MJ, Kilpinen H, Stegle O, Lamond AI. Population-scale proteome variation in human induced pluripotent stem cells. eLife 2020; 9:e57390. [PMID: 32773033 PMCID: PMC7447446 DOI: 10.7554/elife.57390] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 08/08/2020] [Indexed: 12/12/2022] Open
Abstract
Human disease phenotypes are driven primarily by alterations in protein expression and/or function. To date, relatively little is known about the variability of the human proteome in populations and how this relates to variability in mRNA expression and to disease loci. Here, we present the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC), a key cell type for disease modelling, analysing 202 iPSC lines derived from 151 donors, with integrated transcriptome and genomic sequence data from the same lines. We characterised the major genetic and non-genetic determinants of proteome variation across iPSC lines and assessed key regulatory mechanisms affecting variation in protein abundance. We identified 654 protein quantitative trait loci (pQTLs) in iPSCs, including disease-linked variants in protein-coding sequences and variants with trans regulatory effects. These include pQTL linked to GWAS variants that cannot be detected at the mRNA level, highlighting the utility of dissecting pQTL at peptide level resolution.
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Affiliation(s)
- Bogdan Andrei Mirauta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - Daniel D Seaton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - Dalila Bensaddek
- Centre for Gene Regulation & Expression, School of Life Sciences, University of DundeeDundeeUnited Kingdom
| | - Alejandro Brenes
- Centre for Gene Regulation & Expression, School of Life Sciences, University of DundeeDundeeUnited Kingdom
| | - Marc Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - Helena Kilpinen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusHinxtonUnited Kingdom
- European Molecular Biology Laboratory, Genome Biology UnitHeidelbergGermany
- Division of Computational Genomics and Systems Genetic, German Cancer Research CenterHeidelbergGermany
| | - Angus I Lamond
- Centre for Gene Regulation & Expression, School of Life Sciences, University of DundeeDundeeUnited Kingdom
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Phaneuf PV, Yurkovich JT, Heckmann D, Wu M, Sandberg TE, King ZA, Tan J, Palsson BO, Feist AM. Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity. BMC Genomics 2020; 21:514. [PMID: 32711472 PMCID: PMC7382830 DOI: 10.1186/s12864-020-06920-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/17/2020] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. RESULTS Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. CONCLUSIONS The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.
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Affiliation(s)
- Patrick V Phaneuf
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - David Heckmann
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Muyao Wu
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Troy E Sandberg
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Zachary A King
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Justin Tan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Bernhard O Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800, Kgs. Lyngby, Denmark
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA. .,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800, Kgs. Lyngby, Denmark.
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40
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van der Lee R, Correard S, Wasserman WW. Deregulated Regulators: Disease-Causing cis Variants in Transcription Factor Genes. Trends Genet 2020; 36:523-539. [DOI: 10.1016/j.tig.2020.04.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/15/2020] [Accepted: 04/16/2020] [Indexed: 12/12/2022]
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Stephenson JD, Laskowski RA, Nightingale A, Hurles ME, Thornton JM. VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations. Bioinformatics 2020; 35:4854-4856. [PMID: 31192369 PMCID: PMC6853667 DOI: 10.1093/bioinformatics/btz482] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 05/17/2019] [Accepted: 06/07/2019] [Indexed: 12/16/2022] Open
Abstract
Motivation Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence. Results Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information. Availability and implementation https://www.ebi.ac.uk/thornton-srv/databases/VarMap. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- James D Stephenson
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.,Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Roman A Laskowski
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - Andrew Nightingale
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Janet M Thornton
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
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Hackenschmidt S, Bracharz F, Daniel R, Thürmer A, Bruder S, Kabisch J. Effects of a high-cultivation temperature on the physiology of three different Yarrowia lipolytica strains. FEMS Yeast Res 2020; 19:5586564. [PMID: 31605534 DOI: 10.1093/femsyr/foz068] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 10/09/2019] [Indexed: 12/20/2022] Open
Abstract
Despite the increasing relevance, ranging from academic research to industrial applications, only a limited number of non-conventional, oleaginous Yarrowia lipolytica strains are characterized in detail. Therefore, we analyzed three strains in regard to their metabolic and physiological properties, especially with respect to important characteristics of a production strain. By investigating different cultivation conditions and media compositions, similarities and differences between the distinct strain backgrounds could be derived. Especially sugar alcohol production, as well as an agglomeration of cells were found to be connected with growth at high temperatures. In addition, sugar alcohol production was independent of high substrate concentrations under these conditions. To investigate the genotypic basis of particular traits, including growth characteristics and metabolite concentrations, genomic analysis were performed. We found sequence variations for one third of the annotated proteins but no obvious link to all phenotypic features.
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Affiliation(s)
- S Hackenschmidt
- Computergestützte Synthetische Biologie, Technische Universität Darmstadt, Schnittspahnstr. 10, Darmstadt 64287, Germany
| | - F Bracharz
- Computergestützte Synthetische Biologie, Technische Universität Darmstadt, Schnittspahnstr. 10, Darmstadt 64287, Germany
| | - R Daniel
- Department of Genomic and Applied Microbiology, Institute of Microbiology and Genetics, Georg-August University Göttingen, Grisebachstr. 8, 37077 Göttingen, Germany
| | - A Thürmer
- MF 2: Genomsequenzierung, Robert Koch Institute Berlin, Seestrasse 10, 13353 Berlin, Germany
| | - S Bruder
- Computergestützte Synthetische Biologie, Technische Universität Darmstadt, Schnittspahnstr. 10, Darmstadt 64287, Germany
| | - J Kabisch
- Computergestützte Synthetische Biologie, Technische Universität Darmstadt, Schnittspahnstr. 10, Darmstadt 64287, Germany
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43
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Yadav A, Vidal M, Luck K. Precision medicine - networks to the rescue. Curr Opin Biotechnol 2020; 63:177-189. [PMID: 32199228 PMCID: PMC7308189 DOI: 10.1016/j.copbio.2020.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.
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Affiliation(s)
- Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Current address: Institute of Molecular Biology, Mainz, Germany.
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44
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Després PC, Dubé AK, Seki M, Yachie N, Landry CR. Perturbing proteomes at single residue resolution using base editing. Nat Commun 2020; 11:1871. [PMID: 32313011 PMCID: PMC7170841 DOI: 10.1038/s41467-020-15796-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 03/27/2020] [Indexed: 01/18/2023] Open
Abstract
Base editors derived from CRISPR-Cas9 systems and DNA editing enzymes offer an unprecedented opportunity for the precise modification of genes, but have yet to be used at a genome-scale throughput. Here, we test the ability of the Target-AID base editor to systematically modify genes genome-wide by targeting yeast essential genes. We mutate around 17,000 individual sites in parallel across more than 1500 genes. We identify over 700 sites at which mutations have a significant impact on fitness. Using previously determined and preferred Target-AID mutational outcomes, we find that gRNAs with significant effects on fitness are enriched in variants predicted to be deleterious based on residue conservation and predicted protein destabilization. We identify key features influencing effective gRNAs in the context of base editing. Our results show that base editing is a powerful tool to identify key amino acid residues at the scale of proteomes.
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Affiliation(s)
- Philippe C Després
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, G1V 0A6, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, G1V 0A6, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, G1V 0A6, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Alexandre K Dubé
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, G1V 0A6, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, G1V 0A6, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, G1V 0A6, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Motoaki Seki
- Research Center for Advanced Science and Technology, Synthetic Biology Division, University of Tokyo, Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8904, Japan
| | - Nozomu Yachie
- Research Center for Advanced Science and Technology, Synthetic Biology Division, University of Tokyo, Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8904, Japan.
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Tokyo, Japan.
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.
| | - Christian R Landry
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, G1V 0A6, Canada.
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, G1V 0A6, Canada.
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, G1V 0A6, Canada.
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada.
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, G1V 0A6, Canada.
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45
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Laskowski RA, Stephenson JD, Sillitoe I, Orengo CA, Thornton JM. VarSite: Disease variants and protein structure. Protein Sci 2020; 29:111-119. [PMID: 31606900 PMCID: PMC6933866 DOI: 10.1002/pro.3746] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/04/2019] [Accepted: 10/07/2019] [Indexed: 12/20/2022]
Abstract
VarSite is a web server mapping known disease-associated variants from UniProt and ClinVar, together with natural variants from gnomAD, onto protein 3D structures in the Protein Data Bank. The analyses are primarily image-based and provide both an overview for each human protein, as well as a report for any specific variant of interest. The information can be useful in assessing whether a given variant might be pathogenic or benign. The structural annotations for each position in the protein include protein secondary structure, interactions with ligand, metal, DNA/RNA, or other protein, and various measures of a given variant's possible impact on the protein's function. The 3D locations of the disease-associated variants can be viewed interactively via the 3dmol.js JavaScript viewer, as well as in RasMol and PyMOL. Users can search for specific variants, or sets of variants, by providing the DNA coordinates of the base change(s) of interest. Additionally, various agglomerative analyses are given, such as the mapping of disease and natural variants onto specific Pfam or CATH domains. The server is freely accessible to all at: https://www.ebi.ac.uk/thornton-srv/databases/VarSite.
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Affiliation(s)
- Roman A. Laskowski
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)CambridgeUK
| | - James D. Stephenson
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)CambridgeUK
- Wellcome Trust Sanger InstituteCambridgeUK
| | - Ian Sillitoe
- Institute of Structural and Molecular BiologyUniversity College LondonLondonUK
| | - Christine A. Orengo
- Institute of Structural and Molecular BiologyUniversity College LondonLondonUK
| | - Janet M. Thornton
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)CambridgeUK
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46
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Galardini M, Busby BP, Vieitez C, Dunham AS, Typas A, Beltrao P. The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae. Mol Syst Biol 2019; 15:e8831. [PMID: 31885205 PMCID: PMC6901017 DOI: 10.15252/msb.20198831] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 11/13/2019] [Accepted: 11/14/2019] [Indexed: 12/16/2022] Open
Abstract
Loss-of-function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild-type growth differences and the deletion of pleiotropic genes showed above-average changes in phenotypes. In addition, we performed a genome-wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene-condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes.
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Affiliation(s)
- Marco Galardini
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
| | - Bede P Busby
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
- European Molecular Biology LaboratoryGenome Biology UnitHeidelbergGermany
| | - Cristina Vieitez
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
- European Molecular Biology LaboratoryGenome Biology UnitHeidelbergGermany
| | - Alistair S Dunham
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
| | - Athanasios Typas
- European Molecular Biology LaboratoryGenome Biology UnitHeidelbergGermany
| | - Pedro Beltrao
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
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47
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Payen C, Thompson D. The renaissance of yeasts as microbial factories in the modern age of biomanufacturing. Yeast 2019; 36:685-700. [DOI: 10.1002/yea.3439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 07/09/2019] [Accepted: 08/04/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Celia Payen
- DuPont Nutrition and Biosciences Wilmington Delaware
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48
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DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories. PLoS Genet 2019; 15:e1008375. [PMID: 31738765 PMCID: PMC6886874 DOI: 10.1371/journal.pgen.1008375] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 12/02/2019] [Accepted: 10/28/2019] [Indexed: 12/13/2022] Open
Abstract
DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression. Differences in the DNA sequence of individual genomes contribute to differences in many traits, such as appearance, physiology, and the risk for common diseases. An important group of these DNA variants influences how individual genes across the genome are turned on or off. In this paper, we describe a strategy for identifying such “trans-acting” variants in different strains of baker’s yeast. We used this strategy to reveal three single DNA base changes that each influences the expression of dozens of genes. These three DNA variants were very different from each other. Two of them changed the protein sequence, one in a transcription factor and the other in a sugar sensor. The third changed the expression of an enzyme, a change that in turn caused other genes to alter their expression. One variant existed in only a few yeast isolates, while the other two existed in many isolates collected from around the world. This diversity of DNA variants that influence the expression of many other genes illustrates how difficult it is to predict which DNA variants in an individual’s genome will have effects on the organism.
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49
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Abildgaard AB, Stein A, Nielsen SV, Schultz-Knudsen K, Papaleo E, Shrikhande A, Hoffmann ER, Bernstein I, Gerdes AM, Takahashi M, Ishioka C, Lindorff-Larsen K, Hartmann-Petersen R. Computational and cellular studies reveal structural destabilization and degradation of MLH1 variants in Lynch syndrome. eLife 2019; 8:e49138. [PMID: 31697235 PMCID: PMC6837844 DOI: 10.7554/elife.49138] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/23/2019] [Indexed: 12/13/2022] Open
Abstract
Defective mismatch repair leads to increased mutation rates, and germline loss-of-function variants in the repair component MLH1 cause the hereditary cancer predisposition disorder known as Lynch syndrome. Early diagnosis is important, but complicated by many variants being of unknown significance. Here we show that a majority of the disease-linked MLH1 variants we studied are present at reduced cellular levels. We show that destabilized MLH1 variants are targeted for chaperone-assisted proteasomal degradation, resulting also in degradation of co-factors PMS1 and PMS2. In silico saturation mutagenesis and computational predictions of thermodynamic stability of MLH1 missense variants revealed a correlation between structural destabilization, reduced steady-state levels and loss-of-function. Thus, we suggest that loss of stability and cellular degradation is an important mechanism underlying many MLH1 variants in Lynch syndrome. Combined with analyses of conservation, the thermodynamic stability predictions separate disease-linked from benign MLH1 variants, and therefore hold potential for Lynch syndrome diagnostics.
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Affiliation(s)
- Amanda B Abildgaard
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Amelie Stein
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Sofie V Nielsen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Katrine Schultz-Knudsen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Elena Papaleo
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Amruta Shrikhande
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Eva R Hoffmann
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Inge Bernstein
- Department of Surgical GastroenterologyAalborg University HospitalAalborgDenmark
| | | | - Masanobu Takahashi
- Department of Medical OncologyTohoku University Hospital, Tohoku UniversitySendaiJapan
| | - Chikashi Ishioka
- Department of Medical OncologyTohoku University Hospital, Tohoku UniversitySendaiJapan
| | - Kresten Lindorff-Larsen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Rasmus Hartmann-Petersen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
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50
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Oliynyk RT. Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective. Int J Mol Sci 2019; 20:E5013. [PMID: 31658652 PMCID: PMC6834143 DOI: 10.3390/ijms20205013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 10/01/2019] [Accepted: 10/08/2019] [Indexed: 12/15/2022] Open
Abstract
With the accumulation of scientific knowledge of the genetic causes of common diseases and continuous advancement of gene-editing technologies, gene therapies to prevent polygenic diseases may soon become possible. This study endeavored to assess population genetics consequences of such therapies. Computer simulations were used to evaluate the heterogeneity in causal alleles for polygenic diseases that could exist among geographically distinct populations. The results show that although heterogeneity would not be easily detectable by epidemiological studies following population admixture, even significant heterogeneity would not impede the outcomes of preventive gene therapies. Preventive gene therapies designed to correct causal alleles to a naturally-occurring neutral state of nucleotides would lower the prevalence of polygenic early- to middle-age-onset diseases in proportion to the decreased population relative risk attributable to the edited alleles. The outcome would manifest differently for late-onset diseases, for which the therapies would result in a delayed disease onset and decreased lifetime risk; however, the lifetime risk would increase again with prolonging population life expectancy, which is a likely consequence of such therapies. If the preventive heritable gene therapies were to be applied on a large scale, the decreasing frequency of risk alleles in populations would reduce the disease risk or delay the age of onset, even with a fraction of the population receiving such therapies. With ongoing population admixture, all groups would benefit over generations.
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Affiliation(s)
- Roman Teo Oliynyk
- Centre for Computational Evolution, University of Auckland, Auckland 1010, New Zealand.
- Department of Computer Science, University of Auckland, Auckland 1010, New Zealand.
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